Publication
Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation
David Steinmann; Felix Divo; Maurice Kraus; Antonia Wüst; Lukas Struppek; Felix Friedrich; Kristian Kersting
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.05152, Pages 1-31, arXiv, 2024.
Abstract
hortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a
significant challenge in machine learning and AI, critically affecting model generalization and robust-
ness. Research in this area, however, remains fragmented across various terminologies, hindering the
progress of the field as a whole. Consequently, we introduce a unifying taxonomy of shortcut learning
by providing a formal definition of shortcuts and bridging the diverse terms used in the literature. In
doing so, we further establish important connections between shortcuts and related fields, including
bias, causality, and security, where parallels exist but are rarely discussed. Our taxonomy organizes
existing approaches for shortcut detection and mitigation, providing a comprehensive overview of
the current state of the field and revealing underexplored areas and open challenges. Moreover, we
compile and classify datasets tailored to study shortcut learning. Altogether, this work provides a
holistic perspective to deepen understanding and drive the development of more effective strategies
for addressing shortcuts in machine learning.